#59: Should make split_and_compare_prnus_of_subgroups.py compatible with RAW images

datasets/raise/extract_noise.py

@@ -1,8 +1,6 @@
 #!/usr/bin/env python
 
-from skimage import img_as_float
 import numpy as np
-from PIL import Image
 import os
 from tqdm import tqdm
 import csv

datasets/raise/merge_single_color_channel_images_according_to_bayer_filter.py

@@ -1,8 +1,7 @@
 #!/usr/bin/python3
 
 from PIL import Image
-from utils import Color
-from tqdm import tqdm
+from utils import Color, mergeSingleColorChannelImagesAccordingToBayerFilter
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -13,31 +12,7 @@ def getImageByColor(color):
     image = np.load(filePath)
     return image
 
-def getNewIndex(index, offset):
-    newIndex = (index - offset) * 2 + offset
-    return newIndex
-
-for color in tqdm(Color, 'Color'):
-    colorImage = getImageByColor(color)
-    if color == list(Color)[0]:
-        width, height = colorImage.shape
-        multipleColorsImage = np.empty([dimension * 2 for dimension in colorImage.shape], dtype = np.float64)
-    for y in tqdm(range(height), 'Height'):
-        for x in range(width):
-            pixel = colorImage[x, y]
-            '''
-            Assume Bayer Filter:
-            RG
-            GB
-            '''
-            offsetX, offsetY = {
-                Color.RED: (0, 0),
-                Color.GREEN_RIGHT: (1, 0),
-                Color.GREEN_BOTTOM: (0, 1),
-                Color.BLUE: (1, 1),
-            }[color]
-            newX, newY = [getNewIndex(index, offset) for index, offset in [(x, offsetX), (y, offsetY)]]
-
-            multipleColorsImage[newX, newY] = pixel
+singleColorChannelImages = {color: getImageByColor(color) for color in Color}
+multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages, progress = True)
 
 plt.imsave(PREFIX + 'multiple_colors.png', multipleColorsImage)

datasets/raise/split_and_compare_prnus_of_subgroups.py

@@ -1,11 +1,9 @@
-#!/usr/bin/python3
+#!/usr/bin/env python
 
-from PIL import Image
 import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm
-from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color
-from skimage import img_as_float
+from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter
 import sys
 import os
 from random import shuffle
@@ -16,7 +14,7 @@ from rms_diff import rmsDiffNumpy
 
 NUMBER_OF_SUBGROUPS = 2
 DENOISER = 'wavelet'
-IMAGES_FOLDER = 'flat-field/TIF'
+IMAGES_FOLDER = 'flat-field/NEF'
 
 setting = escapeFilePath(IMAGES_FOLDER) + f'_{DENOISER}'
 
@@ -33,9 +31,13 @@ for subgroupImageIndex in tqdm(range(numberOfImagesPerSubgroup), 'Subgroup image
         imageIndex = (subgroupIndex * NUMBER_OF_SUBGROUPS) + subgroupImageIndex
         imageFileName = imagesFileNames[imageIndex]
         imageFilePath = f'{IMAGES_FOLDER}/{imageFileName}'
-        imageNpArray = getColorChannel(imageFilePath, Color.GREEN_RIGHT)
+        singleColorChannelImages = {color: getColorChannel(imageFilePath, color) for color in Color}
+        multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
 
-        imagePrnuEstimateNpArray = imageNpArray - denoise(imageNpArray, DENOISER)
+        singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
+        multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
+
+        imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
         subgroupIterativeMean = subgroupsIterativeMean[subgroupIndex]
         subgroupIterativeMean.add(imagePrnuEstimateNpArray)
         if subgroupIndex == NUMBER_OF_SUBGROUPS - 1:

datasets/raise/utils.py

@@ -2,6 +2,9 @@ from enum import Enum, auto
 import skimage.restoration
 import numpy as np
 import rawpy
+from tqdm import tqdm
+from PIL import Image
+from skimage import img_as_float
 
 class Color(Enum):
     RED = auto()
@@ -70,7 +73,7 @@ def getRawColorChannel(raw, color):
     return imageNpArray
 
 def isARawImage(imageFilePath):
-    return any([imageFileName.lower().endswith(f'.{rawImageFileExtension}') for rawImageFileExtension in RAW_IMAGE_FILE_EXTENSIONS])
+    return any([imageFilePath.lower().endswith(f'.{rawImageFileExtension}') for rawImageFileExtension in RAW_IMAGE_FILE_EXTENSIONS])
 
 def getColorChannel(imageFilePath, color):
     if isARawImage(imageFilePath):
@@ -82,4 +85,38 @@ def getColorChannel(imageFilePath, color):
     return imageNpArray
 
 def escapeFilePath(filePath):
-    return filePath.replace('/', '_')
+    return filePath.replace('/', '_')
+
+
+def getNewIndex(index, offset):
+    newIndex = (index - offset) * 2 + offset
+    return newIndex
+
+def silentTqdm(data, desc = None):
+    return data
+
+def mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages, progress = False):
+    tqdmVersion = tqdm if progress else silentTqdm
+    for color in tqdmVersion(Color, 'Color'):
+        colorImage = singleColorChannelImages[color]
+        if color == list(Color)[0]:
+            width, height = colorImage.shape
+            multipleColorsImage = np.empty([dimension * 2 for dimension in colorImage.shape], dtype = np.float64)
+        for y in tqdmVersion(range(height), 'Height'):
+            for x in range(width):
+                pixel = colorImage[x, y]
+                '''
+                Assume Bayer Filter:
+                RG
+                GB
+                '''
+                offsetX, offsetY = {
+                    Color.RED: (0, 0),
+                    Color.GREEN_RIGHT: (1, 0),
+                    Color.GREEN_BOTTOM: (0, 1),
+                    Color.BLUE: (1, 1),
+                }[color]
+                newX, newY = [getNewIndex(index, offset) for index, offset in [(x, offsetX), (y, offsetY)]]
+
+                multipleColorsImage[newX, newY] = pixel
+    return multipleColorsImage